Every day the amount of data collected from a wide array of sources continues to grow. Sensors may acquire data ranging from temperature, air pressure, wave height, seismic activity, stock prices, commodity prices, and so forth. Where large numbers of sensors are employed, aggregators may receive and consolidate the data from sensors. These aggregators combine data, until a final parent aggregator generates a complete set of sensor data.
A portal, such as a server on the internet, may request information from an aggregator to fulfill a request. Unfortunately, it has traditionally been difficult to provide an assurance that information which has been aggregated has not been corrupted. Such corruption may occur because of system glitches or a malicious actor. Corruption may include increasing the reported value of a sensor (“inflation”) or reducing the reported value (“deflation”).
Various schemes have been put forth to try and secure aggregation. One scheme involves a single entity tightly controlling all aggregators, then making the assumption that these aggregators are not compromised. This scheme has several drawbacks. For example, an organization may not be able to afford the cost of maintaining such a system. Or the organization may not have the technical expertise or geographic reach necessary to maintain the aggregators.
Another scheme to secure aggregation involves the encryption of data at the sensor. This scheme has several drawbacks as well. For example, if the aggregator has the capability to decrypt data, typically considered necessary to aggregate the data, it gains the ability to tamper with the data before sending along to the portal. If the data is not decrypted at the aggregator, the portal is heavily loaded with the task of decrypting and aggregating data itself, removing the benefits of aggregators in the first place.
Thus, there is a desire to outsource aggregation while retaining the capability to determine if sensor data has been inflated or deflated.